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Keyword Search Criteria: Imputation returned 116 record(s)
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Sunday, 07/28/2019
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Approaches to Bias Correction When Using Propensity Scores Estimated from Imperfect EHR-Derived Covariates
Joanna Harton, University of Pennsylvania; Nandita Mitra, University of Pennsylvania; Rebecca Hubbard, University of Pennsylvania
2:50 PM
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Missingness by Design – Split Questionnaire Designs and Synthetic Data
Joerg Drechsler, Institute for Employment Research; Florian Meinfelder, Universität Bamberg
4:05 PM
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Leveraging Auxiliary Information on Marginal Distributions in Nonignorable Models for Item and Unit Nonresponse in Surveys
Olanrewaju Michael Akande, Duke University; Gabriel Madson, Duke University; D. Sunshine Hillygus, Duke University; Jerry Reiter, Duke University
4:05 PM
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A Simulation of Various Missing Data Imputation Methods in the Application of Composite Endpoint
Ja-An Lin, FDA/CDRH; Rajesh Nair, CDRH/FDA; Natasha Sahr, St. Jude's Children's Hospital
4:20 PM
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Missing Data Imputation for Classification Problems
Arkopal Choudhury, University of North Carolina at Chapel Hill; Michael Kosorok, University of North Carolina at Chapel Hill
4:20 PM
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Multiple Imputation of Non-Ignorable Missing Survey Data
Angelina Hammon, University of Bamberg
4:30 PM
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Data Fusion, Multiple Imputation for Clustered Data, and Split Questionnaire Designs: Research Inspired by Our Collaborations with Susie
Trivellore Raghunathan, University of Michigan; Nathaniel Schenker, Retired
4:55 PM
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Test of Treatment Effect for Binary Composite Endpoint with Missing Components in Clinical Trials
Yanyao Yi, University of Wisconsin at Madison; Ting Ye, University of Wisconsin at Madison; Xiang Zhang, Eli Lilly and Company; Junxiang Luo, Sanofi-Aventis
5:05 PM
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Monday, 07/29/2019
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Non Linear Functional Data Imputation
Aniruddha Rajendra Rao, Pennsylvania State University
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An Adapted VAR-EM (AVAR-EM) Imputation Algorithm to Populate a Broken Historical Climate Record
Benjamin Washington, The University of Georgia; Lynne Seymour, University of Georgia
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Prevalence of Sexual Orientation and Gender Identity Behaviors: An Approach for State-Level and National Estimation Derived from the Behavioral Risk Factor Surveillance System
YangYang Deng, ICF Macro, Inc.; Ronaldo Iachan, ICF Macro, Inc.
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Extending Nearest-Neighbor GPs for Non-Gridded Data Imputation
Christopher Grubb, Virginia Tech; Shyam Ranganathan, Virginia Tech
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Multiple Imputation Versus Machine Learning: Predictive Models to Facilitate Analyzes of Association Between Contemporaneous Medicaid/CHIP Enrollment Status and Health Measures
Jennifer Rammon, National Center for Health Statistics/CDC; Yulei He, CDC; Jennifer Parker, CDC/NCHS/OAE/SPB
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A Comparison of Stacked and Pooled Multiple Imputation
Paul Bernhardt, Villanova University
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How Many Imputations Are Enough When Reporting Clinical Trials?
Anders Gorst-Rasmussen, Novo Nordisk A/S
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Hot Deck Imputation Cells for the American Housing Survey
Chrystine Tadler, Insight Policy Research; Richard Griffiths, Insight Policy Research
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Carry Forward Imputation for Unit Non-Response After a Survey Redesign
Kimberly Ault, RTI International
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An Evaluation of Traditional and Machine Learning Imputation Methods for Sampling Frame Construction for the American Voices Project
Cong Ye
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Developing and Evaluating Methods to Impute Race/Ethnicity in an Incomplete Dataset
Gabriella Silva, Brown University; Amal N. Trivedi, Brown University; Roee Gutman, Brown University
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Comparison of Missing Data Imputation Methods in Longitudinal Study of ADRD Patients
Yi Cao, Brown University; Roee Gutman, Brown University; Heather Allore, Yale University ; Brent Vander Wyk, Yale University
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Benefits of Monte Carlo Imputation of Non-Detects in Environmental Data
Kirk Cameron, Macstat Consulting, Ltd.
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Variance Estimation for Nearest Neighbor Imputed Data
Xiaofei Zhang, Iowa State Univ; Wayne Fuller, Iowa State University
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Multiple Imputation for Privacy Protection: Where Are We and Where Are We Going?
Jerry Reiter, Duke University
8:35 AM
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A Generalized Framework to Evaluate Imputation Strategies: Recent Developments
Darren Gray, Statistics Canada
8:35 AM
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Evaluating Imputation Methods for the Agricultural Resource Management Survey
Darcy Miller, National Agricultural Statistics Service; Andrew Dau, National Agricultural Statistics Service; Audra Zakzeski, National Agricultural Statistics Service
8:55 AM
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Multiple Imputation Procedure for Record Linkage and Causal Inference to Estimate the Effects of Home-Delivered Meals
Mingyang Shan, Brown University; Kali Thomas, Brown University; Roee Gutman, Brown University
9:00 AM
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Multiple Imputation Versus Machine Learning: Predictive Models to Facilitate Analyzes of Association Between Contemporaneous Medicaid/CHIP Enrollment Status and Health Measures
Jennifer Rammon, National Center for Health Statistics/CDC; Yulei He, CDC; Jennifer Parker, CDC/NCHS/OAE/SPB
9:10 AM
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Improving Edit and Imputation Strategies Through Feature Selection
Andrew Stelmack, Statistics Canada
9:15 AM
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Application of Multiple Imputation Methodology to Address Measurement Error Problems
Trivellore Raghunathan, University of Michigan
9:25 AM
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Improving Efficiency of Imputation Using Machine Learning
Katie Davies, Office for National Statistics; Vinayak Anand-Kumar, Office for National Statistics
9:35 AM
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A Comparison of Stacked and Pooled Multiple Imputation
Paul Bernhardt, Villanova University
9:50 AM
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Wald I: Statistical Learning with Sparsity
Trevor J. Hastie, Stanford University
10:35 AM
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Finding a Flexible Hot Deck Imputation Method for Multinomial Data
Rebecca Andridge, The Ohio State University College of Public Health; Laura Bechtel, U.S. Census Bureau; Katherine J Thompson, U.S. Census Bureau
10:35 AM
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Nonparametric Mass Imputation for Data Integration
Sixia Chen, University of Oklahoma Health Sciences Center; Jae-kwang Kim, Iowa State University; Shu Yang, North Carolina State University
10:35 AM
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Developing and Evaluating Methods to Impute Race/Ethnicity in an Incomplete Dataset
Gabriella Silva, Brown University; Amal N. Trivedi, Brown University; Roee Gutman, Brown University
10:35 AM
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Evaluation of Imputation Approaches for Disease Diagnosis When Risk Factors Have Missing Values
Katherine E Irimata, National Center for Health Statistics; Guangyu Zhang, National Center for Health Statistics
10:35 AM
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New Insights into Modeling Exposure Measurements Below the Limit of Detection
Ana Maria Ortega-Villa, National Institutes of Health; Danping Liu, National Cancer Institute; Mary H Ward, National Institutes of Health; Albert S Paul, National Institutes of Health
10:35 AM
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Hot Deck Imputation Cells for the American Housing Survey
Chrystine Tadler, Insight Policy Research; Richard Griffiths, Insight Policy Research
10:40 AM
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Comparison of Missing Data Imputation Methods in Longitudinal Study of ADRD Patients
Yi Cao, Brown University; Roee Gutman, Brown University; Heather Allore, Yale University ; Brent Vander Wyk, Yale University
10:45 AM
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Calibrated Imputation Under Edit Restrictions
Ton De Waal, Statistics Netherlands; Jacco Daalmans, Statistics Netherlands
10:55 AM
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A Data-Driven Approach to Cell Ratio Estimation for Item Nonresponse in Survey Sampling
Danhyang Lee, Iowa State University; Jae-kwang Kim, Iowa State University
10:55 AM
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Carry Forward Imputation for Unit Non-Response After a Survey Redesign
Kimberly Ault, RTI International
11:00 AM
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How Many Imputations Are Enough When Reporting Clinical Trials?
Anders Gorst-Rasmussen, Novo Nordisk A/S
11:35 AM
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A Resample-Replace Lasso Procedure for Combining High-Dimensional Markers with Limit of Detection
Yunpeng Zhao, Arizona State Univ; Jinjuan Wang, University of Chinese Academy of Sciences; Larry Tang, George Mason University; Claudius Mueller, George Mason University; Qizhai Li, Academy of Mathematics and Systems Science, Chinese Academy of Science
11:35 AM
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Benefits of Monte Carlo Imputation of Non-Detects in Environmental Data
Kirk Cameron, Macstat Consulting, Ltd.
11:35 AM
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Contrasting a Longitudinal Factor Model with a Linear Mixed-Effects Model to Address Incomplete Data on Repeated Measures in an AIDS Prevention Study
Panteha Hayati Rezvan, University of California Los Angeles; Xiang Lu, University of California Los Angeles; Thomas Belin, UCLA
11:50 AM
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Relative Risk Estimation Using Multiple Imputation with Logistic Regression and Discretization
Jay Xu, University of California, Los Angeles; Thomas Belin, UCLA
11:50 AM
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Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design
Ai Ni, The Ohio State University; Jaya M Satagopan, Memorial Sloan Kettering Cancer Center
11:55 AM
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Incorporating Administrative Data in ACS Editing and Imputation Procedures
Sandra Clark, U.S. Census Bureau
11:55 AM
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Multiple Imputation for Censored Covariate Using Fully Conditional Specification Method
Jingyao Hou; Jing Qian, University of Massachusetts Amherst
12:05 PM
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An Evaluation of Traditional and Machine Learning Imputation Methods for Sampling Frame Construction for the American Voices Project
Cong Ye
12:10 PM
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Variance Estimation for Nearest Neighbor Imputed Data
Xiaofei Zhang, Iowa State Univ; Wayne Fuller, Iowa State University
12:15 PM
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Hierarchical Multi-Resolution Spatial-Temporal Functional Imputation for Large Satellite Image Data
Zhengyuan Zhu, Iowa State University; Weicheng Zhu, Amazon
2:35 PM
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Incorporating Variance and Geographic Specificity into the Imputation Frame Used in Weighting the American Community Survey Group Quarters Sample
Dirk Bullock, U.S. Census Bureau; John M. Jordan, U.S. Census Bureau; Edward C. Castro, Jr., U.S. Census Bureau
2:50 PM
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Imputation as a Practical Alternative to Data Swapping
Saki Kinney, RTI International; David Wilson, RTI International; Alan Karr, RTI International; Kelly Kang, NSF
2:50 PM
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Fully Bayesian Imputation Model for MNAR Data in QPCR
Valeriia Sherina; Matthew N McCall, University of Rochester Medical Center; Tanzy M.T. Love, University of Rochester Medical Center
3:10 PM
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Combining Non-Probability and Probability Survey Samples Through Mass Imputation
Jae-kwang Kim, Iowa State University; Seho Park , Dartmouth University ; Yilin Chen, University of Waterloo; Changbao Wu, University of Waterloo
3:20 PM
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Imputation Models Using Computer Matching Results
Glenn Reisch, United States Census Bureau
3:35 PM
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A Feature Allocation Model for Cytometry by Time-Of-Flight Data
Arthur Lui, University of California - Santa Cruz; Juhee Lee, University of California, Santa Cruz; Peter Thall, U.T. M.D. Anderson Cancer Center; Katy Rezvani, M.D. Anderson Cancer Center
3:45 PM
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Tuesday, 07/30/2019
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Estimating Outcome-Exposure Associations When Exposure Biomarker Detection Limits Vary Across Batches
Jonathan Boss, University of Michigan; Bhramar Mukherjee, University of Michigan; Kelly K. Ferguson, National Institute of Environmental Health Sciences; Amira M. Aker, University of Michigan; Akram N. Alshawabkeh, Northeastern University; Jose F. Cordero, University of Georgia; John D. Meeker, University of Michigan; Sehee Kim, University of Michigan
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A Comparison of Several Missing Data Imputation Techniques for Analyzing Different Types of Missingness
Tiantian Yang, Clemson University; William Bridges, Clemson University
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Missing Data and Sensitivity Analyzes: a Methodology Evolution in Medical Device Studies
Scott Mollan, ICON plc
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A DECAY MODEL for HANDLING MISSING DATA in CLINICAL TRIALS
Tao Sheng
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Statistical Disclosure Control with Machine Learning
Allshine Chen; Sixia Chen, University of Oklahoma Health Sciences Center; Yan Daniel Zhao, University of Oklahoma Health Sciences Center
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A Comparison of Missing Data Imputation Methods for Longitudinal Data
Meghan Sealey; Lan Zhu, Oklahoma State University
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The Comparison of Multiple Imputation and Missing Indicator Methods for Prediction in Regression Analysis
Chi-Hong Tseng, UCLA
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Fully Bayesian Imputation Model for MNAR Data in QPCR
Valeriia Sherina; Matthew N McCall, University of Rochester Medical Center; Tanzy M.T. Love, University of Rochester Medical Center
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A Feature Allocation Model for Cytometry by Time-Of-Flight Data
Arthur Lui, University of California - Santa Cruz; Juhee Lee, University of California, Santa Cruz; Peter Thall, U.T. M.D. Anderson Cancer Center; Katy Rezvani, M.D. Anderson Cancer Center
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Imputation as a Practical Alternative to Data Swapping
Saki Kinney, RTI International; David Wilson, RTI International; Alan Karr, RTI International; Kelly Kang, NSF
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An Imputation Approach for Fitting Random Survival Forests with Interval-Censored Survival Data
Warren Keil; Tyler Cook, University of Central Oklahoma
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Variance Estimation When Combining Inverse Probability Weighting and Multiple Imputation in Electronic Health Records-Based Research
Tanayott Thaweethai, Harvard T.H. Chan School of Public Health; Sebastien Haneuse, Harvard T.H. Chan School of Public Health
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Closest Similar Subset Imputation
Macaulay Okwuokenye, Brio Dexteri Pharmaceutical Consultant & UNE; Karl E Peace, Georgia Southern University
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A Comparison of Methods to Estimate the Event Rate Based on Longitudinal Data
Bo Fu, Astellas Pharma Inc.; Xuan Liu, Astellas Pharma Inc.; Jun Zhao, Astellas Pharma Inc.
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Processing Changes to the Current Population Survey Annual Social and Economic Supplement
Jonathan L. Rothbaum, U.S. Census Bureau; Trudi Jane Renwick, U.S. Census Bureau
8:35 AM
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Simulation Study to Compare Imputation at the ELI-PSU Level Versus the ITEM-AREA Level
Onimissi M Sheidu, Bureau of Labor Statistics
8:35 AM
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An Approach to Multiple Imputation That Avoids the Inclusion of an Outcome in the Imputation Model
Monelle Tamegnon, Janssen R&D
8:35 AM
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An Imputation Approach for Fitting Random Survival Forests with Interval-Censored Survival Data
Warren Keil; Tyler Cook, University of Central Oklahoma
8:45 AM
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Missing Data Imputation with Baseline Information in Longitudinal Clinical Trials
Yilong Zhang, Merck; Zachary Zimmer, Merck; Lei Xu, Merck; Gregory Golm, Merck; Raymond Lam, Merck; Susan Huyck, Merck; Frank G Liu, Merck Sharp & Dohme Inc.
8:50 AM
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Imputing Seasonal Data in an Advanced Indicator with Forecasts from X-13ARIMA-SEATS
Nicole Czaplicki, U.S. Census Bureau; Yarissa Gonzalez, U.S. Census Bureau
8:50 AM
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Changes to the Household Relationship Data in the Current Population Survey
Rose Kreider, U.S. Census Bureau; Benjamin Gurrentz, U.S. Census Bureau
8:55 AM
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Imputation in the American Housing Survey: Comparing Multiple Imputation with Current Hot Deck Methods
Sean Dalby, US Census Bureau
9:05 AM
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Exploring Model Fit Evaluation in Structural Equation Models with Incomplete Ordinal Variables Using the D2 Method
Yu Liu, University of Houston; Suppanut Sriutaisuk, University of Houston
9:20 AM
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An Algorithm of Generalized Robust Ratio Model Estimation for Imputation
Kazumi Wada, National Statistics Center, Japan; Seiji Takata, Shiga University; Hiroe Tsubaki, The Institute of Statistical Mathematics
9:35 AM
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Considerations for the Use of Multiple Imputation in a Noninferiority Trial Setting
Kimberly Walters, Statistics Collaborative, Inc.; Jie Zhou, Statistics Collaborative, Inc.; Janet Wittes, Statistics Collaborative, Inc; Lisa Weissfeld, Stats Collaborative
9:35 AM
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Estimating Treatment Capacity and Annual Client Counts of Substance Abuse Treatment Facilities
Maria DeYoreo
9:35 AM
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A Support Vector Machine Based Semiparametric Mixture Cure Model
Yingwei Peng, Queen's University; Peizhi Li, Dongbei University of Finance and Economics and Queen's University; Qingli Dong, Dongbei University of Finance and Economics and Queen's University
9:50 AM
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Exploring the Performance of IVEware and Proc MI with Ordinal Categorical Data
Valbona Bejleri, USDA National Agricultural Statistics Service; Andrew Dau, National Agricultural Statistics Service; Darcy Miller, National Agricultural Statistics Service
9:50 AM
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Multiple Imputation Strategies for Handling Missing Data When Generalizing Randomized Clinical Trial Findings Through Propensity Score-Based Methodologies
Albee Ling, Stanford University; Maya B Mathur, Harvard University; Kris Kapphahn, Stanford University; Maria Montez-Rath , Stanford University; Manisha Desai, Stanford University Quantitative Sciences Unit
9:50 AM
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An Empirical Study of Correlation Coefficient Aggregation in Multiple Imputation
Jianjun Wang; Xin Ma, University of Kentucky
10:05 AM
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Variance Estimation When Combining Inverse Probability Weighting and Multiple Imputation in Electronic Health Records-Based Research
Tanayott Thaweethai, Harvard T.H. Chan School of Public Health; Sebastien Haneuse, Harvard T.H. Chan School of Public Health
10:05 AM
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Imputation Strategies When a Continuous Outcome Is to Be Dichotomized for Responder Analysis: a Simulation Study
Lysbeth Floden, University of Arizona; Melanie Bell, University of Arizona
10:05 AM
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Incomplete High-Dimensional Inverse Covariance Estimation
Yunxi Zhang, University of Mississippi Medical Center; Soeun Kim, University of Texas Health Science Center at Houston
10:05 AM
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Scalable Gapfilling in Spatio-Temporal Remote Sensing Data
Reinhard Furrer, University of Zurich
10:55 AM
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Integrative Factorization of Bidimensionally Linked Matrices
Eric Lock, University of Minnesota; Jun Young Park, University of Minnesota
11:15 AM
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Closest Similar Subset Imputation
Macaulay Okwuokenye, Brio Dexteri Pharmaceutical Consultant & UNE; Karl E Peace, Georgia Southern University
11:45 AM
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Wald II: Statistical Learning with Sparsity
Trevor J. Hastie, Stanford University
2:05 PM
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Estimation of Average Causal Effect in Clustered Data Using Multiple Imputation
Recai Yucel, SUNY Albany School of Public Health; Meng Wu, Department of Health, NY State
2:05 PM
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A Comparison of Selective Versus Automatic Editing for Estimating Totals
Chin-Fang Weng, U.S. Census Bureau; Joanna Fane Lineback, U.S. Census Bureau
2:35 PM
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Assessment of an Imputation Process Used in the 2017 Census of Agriculture
Tara Murphy, USDA National Agricultural Statistics Service; Habtamu Benecha, NASS/USDA; Denise A. Abreu, USDA National Agricultural Statistics Service; Darcy Miller, National Agricultural Statistics Service
2:50 PM
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Visibility Imputation for Population Size Estimation Using Respondent-Driven Sampling
Katherine McLaughlin, Oregon State University; Mark Handcock, University of California, Los Angles
3:05 PM
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Imputation in a National Health Survey: Balancing Data Quality with Respondent Burden in the Medical Expenditure Panel Survey (MEPS)
Emily Mitchell, Agency for Healthcare Research and Quality; Jerrod Anderson, Agency for Healthcare Research and Quality; Samuel H Zuvekas, Agency for Healthcare Research and Quality
3:20 PM
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Machine Learning European Household Wealth
Johannes Fleck, European University Institute
3:25 PM
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Wednesday, 07/31/2019
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Imputation of Organ Dysfunction Scores in NICU Data MNAR
Lucia Chen, UCLA; David Elashoff, UCLA
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Multilevel Multiple Imputation for Electronic Health Record and Survey Data: Your Flexible Friend
James Robert Carpenter, London School of Hygiene & Tropcial Medicine; Matteo Quartagno, London School of Hygiene & Tropcial Medicine
8:35 AM
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Accurate Correction on Dropout Events in Single-Cell RNAseq Data
Lingling An, University of Arizona; Di Ran, University of Arizona; Shanshan Zhang, University of Arizona; Nick Lytal, University of Arizona
8:35 AM
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Analyzing Tradeoff Between Administrative Records Enumeration and Count Imputation
Andrew Keller, U.S. Census Bureau
8:50 AM
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New Predictive Mean Matching Imputation Methods for Cluster Randomized Trials
Brittney Bailey, Amherst College; Rebecca Andridge, The Ohio State University College of Public Health
9:00 AM
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Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
Roee Gutman, Brown University; Anthony D. Scotina, Simmons University; Robert J Smith, Brown University; Andrew R Zullo, Brown University
9:00 AM
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Wald III: Statistical Learning with Sparsity
Trevor J. Hastie, Stanford University
10:35 AM
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Incomplete Data Analysis of Non-Inferiority Clinical Trials: Difference in Binomial Proportions Case
Yulia Sidi, University of Connecticut; Ofer Harel, Dept of Statistics, U of Connecticut
10:50 AM
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Assessing the Utility of 2015 Medicare Advantage Encounter Data to Improve MCBS Estimates
Holly Hagerty, NORC at the University of Chicago; Nicholas Davis, NORC at the University of Chicago; Michael Trierweiler, NORC at the University of Chicago
3:35 PM
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Thursday, 08/01/2019
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Predicting Events from Longitudinal Data: The Imputed Cox Model
James Troendle, National Institutes of Health; Eric Leifer, National Heart,Lung and Blood Institute; Xin Tian, National Heart, Lung and Blood Institute, National Institutes of Health
9:05 AM
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Statistical Analysis of Longitudinal Data on Riemannian Manifolds
Xiongtao Dai, Iowa State University ; Zhenhua Lin, University of California, Davis; Hans Mueller, UC Davis
9:05 AM
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Transfer Learning in Single Cell Transcriptomics
Nancy Zhang, University of Pennsylvania; Divyansh Agarwal, University of Pennsylvania; Zilu Zhou, University of Pennsylvania; Mo Huang, University of Pennsylvania; Gang Hu, Nankai University; Chengzhong Ye, Tsinghua University; Jingshu Wang, The University of Chicago
9:25 AM
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A Case Study in Bridging for Companion Diagnostic Development: Pembrolizumab and PD-L1 Selected 2nd Line NSCLC Patients
Jared Lunceford, Merck & Co., Inc.; Ellie Corigliano, Merck & Co., Inc.; Siddhartha Mathur, Merck & Co., Inc.; Ziwen Wei, Merck & Co., Inc.; Yue Shentu, Merck & Co., Inc.
10:55 AM
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Balancing Inferential Integrity and Disclosure Risk via Model Targeted Masking and Multiple Imputation
Bei Jiang, University of Alberta; Adrian Raftery, University of Washington; Russell Steele, Mcgill University; Naisyin Wang, U of Michigan
11:15 AM
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